Two prevalent video processing functions include inverse telecine and deinterlacing. In use, a decision as to which of the foregoing types of processing to use is made based on a type of visual data to be processed. Specifically, film-originated, or progressive visual data is typically subjected to inverse telecine processing e.g. “weaving,” etc), while video-originated, or interlaced, visual data is typically subjected to deinterlacing (e.g. “bobbing,” etc.).
In some prior art systems where either type of visual data (e.g. film/video-originated) may be processed, the type of incoming visual data may be determined empirically by inspecting the pixels of the visual data. For example, the pixels may be analyzed to determine a duration of time therebetween. Specifically, if pixels of a given field are approximately 16 ms apart, for example, the visual data is likely video-originated in accordance with the National Television Systems Committee (NTSC) standard. In contrast, if the time separation of frames is 41 ms, the visual data is likely film-originated.
Prior art FIG. 1A illustrates a plurality of frames 10 that show a typical interlacing scheme. Specifically, a first frame 12 and a second frame 14 are shown to include respective images. If such visual data has a video-originated format, a first set of fields 16 is used to display the first frame 12 and a second set of fields 18 is used to display the second frame 14.
Challenges arise when the video-originated format is actually progressive film content, which is telecined or converted to interlaced video. When a deinterlacing circuit receives these pixels, a decision must be made to determine if the interlaced pixels are, in fact, video-originated, or converted progressive film pixels. Each of these scenarios creates unique artifacts that the deinterlacing algorithm and inverse telecine algorithm must detect.
For example, when progressive film content is telecined (e.g. converted to interlaced video with a frame rate conversion of 24 frames/sec (F/S) to 30 F/S in the case of 4801 or 10801), there may be an out-of-sequence field inserted. Typically, 3 progressive frames of film are converted to 5 interlaced frames of video. This is commonly known as 3:2 pull down. When this “bad edit” occurs, the video fields could be reversed or occur out of sequence.
The foregoing artifact may be contrasted with weaving truly video-originated interlaced content that possesses motion. In this case, the motion between fields results in a feathering of the video. See item 20 of FIG. 1A, for example. This is symptomatic of the time difference between the 16 mS fields. Feathering is also symptomatic of field reversal, as in the case of the had telecine edit described above.
When the inverse telecine algorithm is applied, the algorithm typically looks for the “best fit” of fields to create a non-feathered, continuous image or frame. This is similar to fitting pieces of a jigsaw puzzle together. However, when a first stage preprocessing occurs, sometimes this “best fit” judgment gets convoluted, leading to an incorrect choice. The progressive film-based, telecined content could be mistakenly treated as motion video, rather than as “bad edit” film-originated content. The result is unnecessary loss of resolution, as the treatment of the content is a form of bobbing, rather than assembling the correct original fields together.
In addition to the foregoing time-based inspection, pixel movement may also be used to determine whether visual data is film or video-originated. Various characteristics of pixel movement may indicate certain video-specific conditions. For example, if several pixels appear to move backwards, then forwards, and then backwards, this condition may be indicative of a defective telecine process, a had edit, etc., thus signifying film-originated visual data. On the other hand, if pixels exhibit characteristics indicative of the fact that video fields have been improperly assembled (e.g. leading to zig-zags, feathering, etc such scenario may imply the existence of video-originated visual data. Of course, any algorithm may use pixel position and/or motion to empirically determine the appropriate processing for visual data.
One difficulty with this approach occurs when visual data is accompanied with random noise. The presence of such noise may obfuscate the empirical indicators mentioned above, resulting in error in the decision process to either utilize the inverse telecine or deinterlacing processing. This error may, in turn, cause visual defects including, but not limited to feathering, a saw effect, a generally softened image, etc.
While algorithms exist which are adapted to reduce the foregoing noise, they typically involve various transformations that modify the pixels of the visual data. Further, while such processing leads to benefits in the resultant display of the visual data, it may introduce error in the empirical decision to utilize the inverse telecine or deinterlacing when processing the visual data. There is thus a need for addressing these and/or other issues associated with the prior art. It should be noted that similar issues arise in the context of other types of pre-processing, other than noise reduction.